Multi-population inflationary differential evolution algorithm with Adaptive Local Restart

Abstract

In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted.

CAMELOT: Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox

“…MP-AIDEA has been extensively tested over more than 50 test functions, including difficult academic test functions and real-world test problems. Results have shown that the algorithm is averagely very efficient, being always in the first four positions in the ranking obtained comparing its results to those of others algorithms [8].…”

“…These novel equations provide ΔV and the control pattern to achieve a simultaneous variation of the semimajor axis a, inclination i, and right ascension of the ascending node Ω in a given time of flight. More details can be found in [8].…”

“…Multi-population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) is a single-objective global optimiser based on the combination of differential evolution (DE) [26] with local search and local and global restart procedures [8]. The performance of the DE is strongly influenced by the setting of its two parameters, the differential weight F and the crossover probability CR, whose best settings are heavily problem dependent [5,11].…”

Statistical multi-criteria evaluation of non-nuclear asteroid deflection methods

“…Furthermore, it is desirable to investigate the trade-off between warning time and achievable miss distance. For this reason we used two global optimisation procedures one for single objective and the other for multi-objective optimisation of multi-modal functions: MP-AIDEA (Di Carlo et al, 2015) and MACS2 (Ricciardi and Vasile, 2015). In the following we briefly present how each optimisation approach works and how it was used in the context of this paper.…”

“…MP-AIDEA extends this concept by automatically adapting some key parameters governing the convergence of the algorithm. MP-AIDEA has been extensively tested on a range of difficult problems including real-world applications (Di Carlo et al, 2015).…”